OdiEnCorp: Odia–English and Odia-Only Corpus for Machine Translation

Parida, Shantipriya; Bojar, Ondřej; Dash, Satya Ranjan

doi:10.1007/978-981-13-9282-5_47

Cited by 12 publications

(6 citation statements)

References 18 publications

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“…The machine translation evaluation matrices BLEU (Papineni et al, 2002) and ChrF (Popović, 2017) used by the organizers to evaluate the submissions. Based on our observation, the statistical approach performed well as compared to NMT for many language pairs as shown in the Table 2 (Parida et al, 2019). Also, among NMT model settings one-to-one and oneto-many perform well based on the language pairs.…”

Section: Resultsmentioning

confidence: 66%

Open Machine Translation for Low Resource South American Languages (AmericasNLP 2021 Shared Task Contribution)

Parida¹,

Panda²,

Dash³

et al. 2021

Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas

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This paper describes the team ("Tamalli")'s submission to AmericasNLP2021 shared task on Open Machine Translation for low resource South American languages. Our goal was to evaluate different Machine Translation (MT) techniques, statistical and neural-based, under several configuration settings. We obtained the second-best results for the language pairs "Spanish-Bribri", "Spanish-Asháninka", and "Spanish-Rarámuri" in the category "Development set not used for training". Our performed experiments will serve as a point of reference for researchers working on MT with low-resource languages.

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Section: Resultsmentioning

confidence: 66%

Open Machine Translation for Low Resource South American Languages (AmericasNLP 2021 Shared Task Contribution)

Parida¹,

Panda²,

Dash³

et al. 2021

Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas

Self Cite

View full text Add to dashboard Cite

show abstract

“…The machine translation evaluation matrices BLEU and ChrF used by the organizers to evaluate the submissions. Based on our observation, the statistical approach performed well as compared to NMT for many language pairs as shown in the Table 2 (Parida et al, 2019). Also, among NMT model settings one-to-one and oneto-many perform well based on the language pairs.…”

Section: Resultsmentioning

confidence: 66%

Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas

Mager¹,

Oncevay²,

Rios³

et al. 2021

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This area is in all probability unmatched, anywhere in the world, in its linguistic multiplicity and diversity. A couple of thousand languages and dialects, at present divided into 17 large families and 38 small ones, with several hundred unclassified single languages, are on record. In one small portion of the area, in Mexico just north of the Isthmus of Tehuantepec, one finds a diversity of linguistic type hard to match on an entire continent in the Old World.

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“…This leads to depending entirely on accurate document pair retrieval for extracting a corpus of reasonable alignment accuracy. The preliminary efforts used to retrieve and align data in Siripragada et al [2020] comprises of Bible [Parida et al 2020] 2 and 4 we observe increase in BLEU scores when translating to English. This leads to better retrieval and sentence level accuracies.…”

Section: Discussionmentioning

confidence: 70%

Revisiting Low Resource Status of Indian Languages in Machine Translation

Philip,

Siripragada,

Namboodiri

et al. 2020

Preprint

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Indian language machine translation performance is hampered due to the lack of large scale multi-lingual sentence aligned corpora and robust benchmarks. Through this paper, we provide and analyse an automated framework to obtain such a corpus for Indian language neural machine translation (NMT) systems. Our pipeline consists of a baseline NMT system, a retrieval module, and an alignment module that is used to work with publicly available websites such as press releases by the government. The main contribution towards this effort is to obtain an incremental method that uses the above pipeline to iteratively improve the size of the corpus as well as improve each of the components of our system. Through our work, we also evaluate the design choices such as the choice of pivoting language and the effect of iterative incremental increase in corpus size. Our work in addition to providing an automated framework also results in generating a relatively larger corpus as compared to existing corpora that are available for Indian languages. This corpus helps us obtain substantially improved results on the publicly available WAT evaluation benchmark and other standard evaluation benchmarks.

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OdiEnCorp: Odia–English and Odia-Only Corpus for Machine Translation

Cited by 12 publications

References 18 publications

Open Machine Translation for Low Resource South American Languages (AmericasNLP 2021 Shared Task Contribution)

Open Machine Translation for Low Resource South American Languages (AmericasNLP 2021 Shared Task Contribution)

Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas

Revisiting Low Resource Status of Indian Languages in Machine Translation

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